More efficient detection of social security fraud

SAS data mining does the trick at FPS Social Security

Social fraud causes a significant loss of income, affecting the financing of Belgium's social security. The fight against this crime is increasingly complicated due to the emergence of sophisticated new types of offences that are difficult to detect. Data mining tools are proving to be a valuable weapon to expose these criminal activities. The Federal Public Service (FPS) Social Security is succeeding—with unchanged staffing levels—in detecting significantly more cases of fraud, thanks to SAS.

The inspection services of the FPS Social Security focus on employers who try to escape their obligations for the co-financing of social security in our country. The most common offences include undeclared work, abuse of the part-time schemes, and the use of bogus self-employed people, to name just a few. The FPS has a staff of only 244 people, tasked with inspecting nearly 250,000 employers. Tight budgetary constraints do not allow an increase in staffing levels to boost the fight against fraudsters whose techniques are continuously evolving.

It was possible to meet this ambitious objective without major technical or organizational problems, thanks to the excellent coordination between all participants.

Gaël Kermarrec
project manager at The Social Inspectorate of the FPS Social Security

Creative fraudsters more easily detected

The principal behavioural indicators that the inspectors have relied on to focus their inspections have an increasingly short shelf life. In fact most have a maximum of only two to three years. "The creativity of the fraudsters requires us to work much more quickly", sums up Gaël Kermarrec, project manager at the social inspectorate.

One example concerns the misuse of the new rules regarding the intra-Community posting of workers in the construction sector. Sébastien Kondov, data mining account manager at the FPS Social Security adds some perspective to the enormity of the problem. "We know the vulnerable sectors—cleaning, construction, and, in certain cases, transport—but it remains very difficult to detect and then accurately measure cases of fraud. Approximately 70% of the investigations conducted by our services are imposed on us following complaints or by the courts. The remaining 30% are inspections on our own initiative. It is for the latter mission that we especially want to optimize our efficiency."

In the end, we met this ambitious objective without major technical or organizational problems, thanks to the excellent coordination between all of the participants

Automated predictive models

The FPS Social Security started searching for a data mining tool that could enable them to achieve all of these efficiency gains. After winning an initial call for tenders for a proof of concept, SAS followed up that success by winning the contract for the implementation of a complete data mining solution.

SAS's data mining tools are integrated into an enlarged data warehouse that had already been in use for nearly a decade. "The biggest challenge we faced in incorporating data mining was to automate our predictive models and thereby improve our responsiveness while at the same time being less dependent on the IT department," explains Sébastien Kondov. "Moreover, we needed an analytical tool capable of prioritizing the alert levels and the risk profiles."Previously, the defining of alerts concerning fraud risks required a considerable investment of time on the part of the analysts and had to be implemented on a case-by-case basis by the IT department.

Committed and keen users

In addition to the core project team of 10 people (analysts, IT staff, and inspectors from the FPS Social Security) with specific training in SAS tools, another 40 people actively participated in the deployment. Gaël Kermarrec also observesthat "A task force of auditors and inspectors is set up to verify that the predictive models defined by the core team can be easily interpreted in the field for each type of fraud. A particularly gratifying occurrence has been that the inspectors themselves are keen to have suggestions to guide them in their investigations. We noticed a robust and positive change in attitude during the very first months of the project."

A tripled detection rate

The first improved results were not long in coming: "The most optimistic scenarios projected that the average detection rate would rise from 16% to 45% among the employer profiles identified as being a higher risk in terms of temporary lay-off fraud in the construction sector. In fact, we exceeded a detection rate of 50% at the initial inspections in May and June," explains Gaël Kermarrec.

Benefits of sharing experience

The other objective sought through the implementation of this data mining solution was to facilitate the sharing of knowledge and experience between the inspectors. They work in a decentralized manner, and often spend far more time in the field than in the office, leaving little opportunity for exchanges. The data mining platform now acts as a knowledge management tool in this respect, making it possible to rapidly access the statistics and experiences of colleagues in other regions of the country.

In six months

The main implementation challenge was the timing. The FPS gave itself just six months to build a platform capable of providing initial concrete results. "In the end, we met this ambitious objective without major technical or organizational problems, thanks to the excellent coordination between all of the participants," observes Sébastien Kondov.

Towards a routine tool to support decision-making

The FPS Social Security is now equipped to look even further ahead. Gaël Kermarrec notes that "The priority for the coming months is to optimize the tool, to further improve the models and the scoring, and to expand the scope of its use, depending on the type of fraud. In the medium term, our ambition is to develop a support tool for decision-making integrated into routine processes. We envision data mining assistance that can be used directly by all of our inspectors."

Challenge

Optimizing social fraud detection without expanding the number of inspectors

Solution

Benefits

Enhanced flexibility to deal with the increasing complexity and sophistication of frauds

Greater autonomy for the social inspectors increases motivation

More knowledge sharing between inspectors

Lessons Learned

Never underestimate the complexity of data. Even though social fraud detection services can rely on good quality data, it is very important to take enough time to optimize these data. It is always good to have high-level analysts within the organization.

Involve the business users (the inspectors and controllers in this case) from the very start of the project. This ensures comprehensive and enthusiastic adoption of the tool.

Let the business users validate the models utilizing their experience in the field to ensure the accuracy of the tool.

Facts and figures

Target group of 244 inspectors active in fighting social fraud
Approximately 50 Gb of data used for the data mining tables
Regularization proposals approaching 110 million euro per year made by SPF Social Security to employers who committed social fraud

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.